ModelPsfMatchTask¶

class lsst.ip.diffim.ModelPsfMatchTask(*args, **kwargs)¶

Bases: lsst.ip.diffim.PsfMatchTask

Matching of two model Psfs, and application of the Psf-matching kernel to an input Exposure

Notes

This Task differs from ImagePsfMatchTask in that it matches two Psf _models_, by realizing them in an Exposure-sized SpatialCellSet and then inserting each Psf-image pair into KernelCandidates. Because none of the pairs of sources that are to be matched should be invalid, all sigma clipping is turned off in ModelPsfMatchConfig. And because there is no tracked _variance_ in the Psf images, the debugging and logging QA info should be interpreted with caution.

One item of note is that the sizes of Psf models are fixed (e.g. its defined as a 21x21 matrix). When the Psf-matching kernel is being solved for, the Psf “image” is convolved with each kernel basis function, leading to a loss of information around the borders. This pixel loss will be problematic for the numerical stability of the kernel solution if the size of the convolution kernel (set by ModelPsfMatchConfig.kernelSize) is much bigger than: psfSize//2. Thus the sizes of Psf-model matching kernels are typically smaller than their image-matching counterparts. If the size of the kernel is too small, the convolved stars will look “boxy”; if the kernel is too large, the kernel solution will be “noisy”. This is a trade-off that needs careful attention for a given dataset.

The primary use case for this Task is in matching an Exposure to a constant-across-the-sky Psf model for the purposes of image coaddition. It is important to note that in the code, the “template” Psf is the Psf that the science image gets matched to. In this sense the order of template and science image are reversed, compared to ImagePsfMatchTask, which operates on the template image.

Debug variables

The pipetask command line interface supports a flag –debug to import @b debug.py from your PYTHONPATH. The relevant contents of debug.py for this Task include:

import sys
import lsstDebug
def DebugInfo(name):
    di = lsstDebug.getInfo(name)
    if name == "lsst.ip.diffim.psfMatch":
        di.display = True                 # global
        di.maskTransparency = 80          # mask transparency
        di.displayCandidates = True       # show all the candidates and residuals
        di.displayKernelBasis = False     # show kernel basis functions
        di.displayKernelMosaic = True     # show kernel realized across the image
        di.plotKernelSpatialModel = False # show coefficients of spatial model
        di.showBadCandidates = True       # show the bad candidates (red) along with good (green)
    elif name == "lsst.ip.diffim.modelPsfMatch":
        di.display = True                 # global
        di.maskTransparency = 30          # mask transparency
        di.displaySpatialCells = True     # show spatial cells before the fit
    return di
lsstDebug.Info = DebugInfo
lsstDebug.frame = 1

Note that if you want addional logging info, you may add to your scripts:

import lsst.utils.logging as logUtils
logUtils.trace_set_at("lsst.ip.diffim", 4)

Examples

A complete example of using ModelPsfMatchTask

This code is modelPsfMatchTask.py in the examples directory, and can be run as e.g.

examples/modelPsfMatchTask.py
examples/modelPsfMatchTask.py --debug
examples/modelPsfMatchTask.py --debug --template /path/to/templateExp.fits
--science /path/to/scienceExp.fits

Create a subclass of ModelPsfMatchTask that accepts two exposures. Note that the “template” exposure contains the Psf that will get matched to, and the “science” exposure is the one that will be convolved:

class MyModelPsfMatchTask(ModelPsfMatchTask):
    def __init__(self, *args, **kwargs):
        ModelPsfMatchTask.__init__(self, *args, **kwargs)
    def run(self, templateExp, scienceExp):
        return ModelPsfMatchTask.run(self, scienceExp, templateExp.getPsf())

And allow the user the freedom to either run the script in default mode, or point to their own images on disk. Note that these images must be readable as an lsst.afw.image.Exposure:

if __name__ == "__main__":
    import argparse
    parser = argparse.ArgumentParser(description="Demonstrate the use of ModelPsfMatchTask")
    parser.add_argument("--debug", "-d", action="store_true", help="Load debug.py?", default=False)
    parser.add_argument("--template", "-t", help="Template Exposure to use", default=None)
    parser.add_argument("--science", "-s", help="Science Exposure to use", default=None)
    args = parser.parse_args()

We have enabled some minor display debugging in this script via the –debug option. However, if you have an lsstDebug debug.py in your PYTHONPATH you will get additional debugging displays. The following block checks for this script:

if args.debug:
    try:
        import debug
        # Since I am displaying 2 images here, set the starting frame number for the LSST debug LSST
        debug.lsstDebug.frame = 3
    except ImportError as e:
        print(e, file=sys.stderr)

Finally, we call a run method that we define below. First set up a Config and modify some of the parameters. In particular we don’t want to “grow” the sizes of the kernel or KernelCandidates, since we are operating with fixed–size images (i.e. the size of the input Psf models).

def run(args):
    #
    # Create the Config and use sum of gaussian basis
    #
    config = ModelPsfMatchTask.ConfigClass()
    config.kernel.active.scaleByFwhm = False

Make sure the images (if any) that were sent to the script exist on disk and are readable. If no images are sent, make some fake data up for the sake of this example script (have a look at the code if you want more details on generateFakeData):

# Run the requested method of the Task
if args.template is not None and args.science is not None:
    if not os.path.isfile(args.template):
        raise FileNotFoundError("Template image %s does not exist" % (args.template))
    if not os.path.isfile(args.science):
        raise FileNotFoundError("Science image %s does not exist" % (args.science))
    try:
        templateExp = afwImage.ExposureF(args.template)
    except Exception as e:
        raise RuntimeError("Cannot read template image %s" % (args.template))
    try:
        scienceExp = afwImage.ExposureF(args.science)
    except Exception as e:
        raise RuntimeError("Cannot read science image %s" % (args.science))
else:
    templateExp, scienceExp = generateFakeData()
    config.kernel.active.sizeCellX = 128
    config.kernel.active.sizeCellY = 128

if args.debug:
    afwDisplay.Display(frame=1).mtv(templateExp, title="Example script: Input Template")
    afwDisplay.Display(frame=2).mtv(scienceExp, title="Example script: Input Science Image")

Create and run the Task:

# Create the Task
psfMatchTask = MyModelPsfMatchTask(config=config)
# Run the Task
result = psfMatchTask.run(templateExp, scienceExp)

And finally provide optional debugging display of the Psf-matched (via the Psf models) science image:

if args.debug:
    # See if the LSST debug has incremented the frame number; if not start with frame 3
    try:
        frame = debug.lsstDebug.frame + 1
    except Exception:
        frame = 3
    afwDisplay.Display(frame=frame).mtv(result.psfMatchedExposure,
                                        title="Example script: Matched Science Image")

Methods Summary

`emptyMetadata`()	Empty (clear) the metadata for this Task and all sub-Tasks.
`getAllSchemaCatalogs`()	Get schema catalogs for all tasks in the hierarchy, combining the results into a single dict.
`getFullMetadata`()	Get metadata for all tasks.
`getFullName`()	Get the task name as a hierarchical name including parent task names.
`getName`()	Get the name of the task.
`getSchemaCatalogs`()	Get the schemas generated by this task.
`getTaskDict`()	Get a dictionary of all tasks as a shallow copy.
`makeField`(doc)	Make a `lsst.pex.config.ConfigurableField` for this task.
`makeSubtask`(name, **keyArgs)	Create a subtask as a new instance as the `name` attribute of this task.
`run`(exposure, referencePsfModel[, kernelSum])	Psf-match an exposure to a model Psf
`timer`(name, logLevel)	Context manager to log performance data for an arbitrary block of code.

Methods Documentation

emptyMetadata() → None¶: Empty (clear) the metadata for this Task and all sub-Tasks.

getAllSchemaCatalogs() → Dict[str, Any]¶

Get schema catalogs for all tasks in the hierarchy, combining the results into a single dict.

Returns:	schemacatalogs : `dict` Keys are butler dataset type, values are a empty catalog (an instance of the appropriate `lsst.afw.table` Catalog type) for all tasks in the hierarchy, from the top-level task down through all subtasks.

Notes

This method may be called on any task in the hierarchy; it will return the same answer, regardless.

The default implementation should always suffice. If your subtask uses schemas the override Task.getSchemaCatalogs, not this method.

getFullMetadata() → lsst.pipe.base._task_metadata.TaskMetadata¶

Get metadata for all tasks.

Returns:	metadata : `TaskMetadata` The keys are the full task name. Values are metadata for the top-level task and all subtasks, sub-subtasks, etc.

Notes

The returned metadata includes timing information (if @timer.timeMethod is used) and any metadata set by the task. The name of each item consists of the full task name with . replaced by :, followed by . and the name of the item, e.g.:

topLevelTaskName:subtaskName:subsubtaskName.itemName

using : in the full task name disambiguates the rare situation that a task has a subtask and a metadata item with the same name.

getFullName() → str¶

Get the task name as a hierarchical name including parent task names.

Returns:	fullName : `str` The full name consists of the name of the parent task and each subtask separated by periods. For example: The full name of top-level task “top” is simply “top”. The full name of subtask “sub” of top-level task “top” is “top.sub”. The full name of subtask “sub2” of subtask “sub” of top-level task “top” is “top.sub.sub2”.

getName() → str¶

Get the name of the task.

Returns:	taskName : `str` Name of the task.

See also

Task.getAllSchemaCatalogs

Notes

Warning

Subclasses that use schemas must override this method. The default implementation returns an empty dict.

This method may be called at any time after the Task is constructed, which means that all task schemas should be computed at construction time, not when data is actually processed. This reflects the philosophy that the schema should not depend on the data.

Returning catalogs rather than just schemas allows us to save e.g. slots for SourceCatalog as well.

getTaskDict() → Dict[str, weakref]¶

Get a dictionary of all tasks as a shallow copy.

Returns:	taskDict : `dict` Dictionary containing full task name: task object for the top-level task and all subtasks, sub-subtasks, etc.

classmethod makeField(doc: str) → lsst.pex.config.configurableField.ConfigurableField¶

Make a lsst.pex.config.ConfigurableField for this task.

Parameters:	doc : `str` Help text for the field.
Returns:	configurableField : `lsst.pex.config.ConfigurableField` A `ConfigurableField` for this task.

Examples

Provides a convenient way to specify this task is a subtask of another task.

Here is an example of use:

class OtherTaskConfig(lsst.pex.config.Config):
    aSubtask = ATaskClass.makeField("brief description of task")

makeSubtask(name: str, **keyArgs) → None¶

Create a subtask as a new instance as the name attribute of this task.

Parameters:	name : `str` Brief name of the subtask. keyArgs Extra keyword arguments used to construct the task. The following arguments are automatically provided and cannot be overridden: “config”. “parentTask”.

Notes

The subtask must be defined by Task.config.name, an instance of ConfigurableField or RegistryField.

run(exposure, referencePsfModel, kernelSum=1.0)¶

Psf-match an exposure to a model Psf

Parameters:	exposure : `lsst.afw.image.Exposure` Exposure to Psf-match to the reference Psf model; it must return a valid PSF model via exposure.getPsf() referencePsfModel : `lsst.afw.detection.Psf` The Psf model to match to kernelSum : `float`, optional A multipicative factor to apply to the kernel sum (default=1.0)
Returns:	result : `struct` `psfMatchedExposure` : the Psf-matched Exposure. This has the same parent bbox, Wcs, PhotoCalib and Filter as the input Exposure but no Psf. In theory the Psf should equal referencePsfModel but the match is likely not exact. `psfMatchingKernel` : the spatially varying Psf-matching kernel `kernelCellSet` : SpatialCellSet used to solve for the Psf-matching kernel `referencePsfModel` : Validated and/or modified reference model used
Raises:	RuntimeError if the Exposure does not contain a Psf model

timer(name: str, logLevel: int = 10) → Iterator[None]¶

Context manager to log performance data for an arbitrary block of code.

Parameters:	name : `str` Name of code being timed; data will be logged using item name: `Start` and `End`. logLevel A `logging` level constant.

Navigation

ModelPsfMatchTask¶